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2022 IEEE Symposium on Industrial Electronics and Applications, ISIEA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2052038


The rapid advancement of social networks and the convenience of internet availability have accelerated the rampant spread of false news and rumors on social media sites. Amid the COVID-19 epidemic, this misleading information has aggravated the situation by putting people's mental and physical lives in danger. To limit the spread of such inaccuracies, identifying the fake news from online platforms could be the first and foremost step. In this research, the authors have conducted a comparative analysis by implementing five transformer-based models such as BERT, BERT without LSTM, ALBERT, RoBERTa, and a Hybrid of BERT & ALBERT in order to detect the fraudulent news of COVID-19 from the internet. COVID-19 Fake News Dataset has been used for training and testing the models. Among all these models, the RoBERTa model has performed better than other models by obtaining an F1 score of 0.98 in both real and fake classes. © 2022 IEEE.

4th International Conference on Bio-Engineering for Smart Technologies, BioSMART 2021 ; 2021.
Article in English | Scopus | ID: covidwho-1730906


The number of people affected by Coronavirus is quite concerning in Bangladesh. It has become a necessity to forecast the future cases since it involves ensuring adequate resources to help people and imposing strict guidelines to deal with this epidemic. This research is about predicting upcoming COVID-19 confirmed cases and deaths from a time series dataset using Hidden Markov Model. The optimal number of hidden states were determined using AIC and BIC. The proposed models are implemented to forecast the daily confirmed cases and daily deaths of Bangladesh for next 90 days. © 2021 IEEE.